no code implementations • 14 Sep 2016 • V. Abrol, O. Absil, P. -A. Absil, S. Anthoine, P. Antoine, T. Arildsen, N. Bertin, F. Bleichrodt, J. Bobin, A. Bol, A. Bonnefoy, F. Caltagirone, V. Cambareri, C. Chenot, V. Crnojević, M. Daňková, K. Degraux, J. Eisert, J. M. Fadili, M. Gabrié, N. Gac, D. Giacobello, A. Gonzalez, C. A. Gomez Gonzalez, A. González, P. -Y. Gousenbourger, M. Græsbøll Christensen, R. Gribonval, S. Guérit, S. Huang, P. Irofti, L. Jacques, U. S. Kamilov, S. Kiticć, M. Kliesch, F. Krzakala, J. A. Lee, W. Liao, T. Lindstrøm Jensen, A. Manoel, H. Mansour, A. Mohammad-Djafari, A. Moshtaghpour, F. Ngolè, B. Pairet, M. Panić, G. Peyré, A. Pižurica, P. Rajmic, M. Roblin, I. Roth, A. K. Sao, P. Sharma, J. -L. Starck, E. W. Tramel, T. van Waterschoot, D. Vukobratovic, L. Wang, B. Wirth, G. Wunder, H. Zhang
The third edition of the "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) took place in Aalborg, the 4th largest city in Denmark situated beautifully in the northern part of the country, from the 24th to 26th of August 2016.
Exploiting this envelope preserving property of CS samples, we propose a new fast dictionary learning (DL) algorithm which is able to extract prototype signals from compressive samples for efficient sparse representation and recovery of signals.